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Updated: 36 min 47 sec ago

Show HN: A virtual machine in the Rust type system

Tue, 03/03/2026 - 6:05am

As a follow-up to a CTF challenge I wrote, I created a small proof-of-concept virtual machine embedded in the Rust type system. Trait resolution and in particular associated types are used to "execute" a program, represented as a type, to its final state. (The "execution" happens during Rust compilation.)

The VM architecture is simple: two 8-bit registers, a stack, a small number of instructions, including conditional control flow. However, other than patience and hitting the recursion limits, more useful/realistic architectures could be embedded as well.

I wrote a blog post that goes into more depth about how this was developed and how it all works: https://www.thenet.sk/blog0011

Comments URL: https://news.ycombinator.com/item?id=47230768

Points: 2

# Comments: 0

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Continuum – CI drift guard for LLM workflows

Tue, 03/03/2026 - 6:03am
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Show HN: The Content Repurposing Fallacy: AI Clips Underperform

Tue, 03/03/2026 - 6:02am

The Content Repurposing Fallacy: Why Your AI-Generated Clips Underperform (And How to Fix It)

Most teams think repurposing = cutting a long video into clips and posting everywhere. They're wrong. We tested this for 3 months with naive repurposing:

Platform Avg Views Engagement Cost/Engaging View TikTok 187 1.2% $2.84 Instagram Reels 243 1.8% $2.11 YouTube Shorts 156 0.9% $3.47 Twitter 89 0.7% $4.22 LinkedIn 201 2.1% $1.89

Time: 8h/week, $0 ad spend. Results were terrible.

Then we switched to strategic platform-native repurposing using AI automation. Results in 90 days:

- Multi-platform reach: 317% increase - Time spent: 12+ hours/week saved - Lead attribution: 28% uplift - Engagement rate: 300% increase - Leads: 675% increase - Cost per lead: 87% reduction

Our framework: "One Core, Many Faces"

1. Pillar Content Audit: Score content on evergreen, actionable, emotional, and platform fit. Only 7+ scores get full treatment.

2. Platform-Specific Angles: Each platform gets a unique angle that serves its audience's intent: - Hacker News: Technical deep-dive with code - Reddit: Discussion prompt with vulnerability - LinkedIn: Professional lesson with metrics - Twitter: Thread narrative with hook-train-CTA - Medium/Dev.to: How-to guide with examples - Newsletter: Curated digest with personal notes - Video: Visual demo or storytelling

3. AI-Assisted Transformation: Use AI to generate platform-specific outlines, maintain brand voice, and auto-assemble formats.

We built this stack with both commercial and open-source tools. Total cost: ~$357/month for commercial, or ~$50/month with DIY open-source (Whisper + GPT-4/Claude + FastAPI + Supabase).

Key insight: Quality of adaptation matters more than quantity. AI handled the heavy lifting; human time went into editing and community engagement.

Content repurposing is not cut-and-paste. It's platform-native translation.

If you're creating long-form content but it's not working harder for you across channels, the problem isn't lack of effort—it's lack of platform-native strategy.

What's your biggest content distribution challenge?

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(Full writeup with detailed metrics and framework checklist available.)

Comments URL: https://news.ycombinator.com/item?id=47230750

Points: 1

# Comments: 0

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Open-Source Article 12 Logging Infrastructure for the EU AI Act in TypeScript

Tue, 03/03/2026 - 5:11am

EU legislation (which affects UK and US companies in many cases) requires being able to truly reconstruct agentic events.

I've worked in a number of regulated industries off & on for years, and recently hit this gap.

We already had strong observability, but if someone asked me to prove exactly what happened for a specific AI decision X months ago (and demonstrate that the log trail had not been altered), I could not.

The EU AI Act has already entered force, and its Article 12 kicks-in in August this year, requiring automatic event recording and six-month retention for high-risk systems, which many legal commentators have suggested reads more like an append-only ledger requirement than standard application logging.

With this in mind, we built a small free, open-source TypeScript library for Node apps using the Vercel AI SDK that captures inference as an append-only log.

It wraps the model in middleware, automatically logs every inference call to structured JSONL in your own S3 bucket, chains entries with SHA-256 hashes for tamper detection, enforces a 180-day retention floor, and provides a CLI to reconstruct a decision and verify integrity. There is also a coverage command that flags likely gaps (in practice omissions are a bigger risk than edits).

The library is deliberately simple: TS, targeting Vercel AI SDK middleware, S3 or local fs, linear hash chaining. It also works with Mastra (agentic framework), and I am happy to expand its integrations via PRs.

Blog post with link to repo: https://systima.ai/blog/open-source-article-12-audit-logging

I'd value feedback, thoughts, and any critique.

Comments URL: https://news.ycombinator.com/item?id=47230438

Points: 1

# Comments: 0

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Ask HN: What are you working on (March 2026)

Tue, 03/03/2026 - 5:02am

Comments URL: https://news.ycombinator.com/item?id=47230384

Points: 2

# Comments: 0

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Show HN: I Built Systems AGI – 1600 Verticals, Self-Healing, Self-Evolving

Tue, 03/03/2026 - 5:01am

Hey HN,

For a while now, I've been building a cognitive AI business operating system from the ground up natively as a solo developer. I never coded before 6 months ago. I was frustrated with wrappers and fragile single-agent setups, so I decided to approach this as a distributed systems problem instead. It's called *Sophify*.

Instead of trying to make one monolithic capability do everything, I built a 12-server, 28+ microservice swarm orchestrated by over 40 highly specialized cognitive AI agents.

*Here’s the architecture under the hood:* * *40+ Agent Architecture (Model Agnostic):* Decisions aren’t made by a single prompted agent. There is a multi-agent orchestration system that uses weighted voting and consensus across 40+ specialized cognitive agent profiles to synthesize decisions. Crucially, the system is LLM-agnostic—you can plug in whatever LLM you prefer (local or remote) to power the reasoning engines. * *The Technician Bot:* Given that this comprises 28+ interconnected microservices running in the cloud. I wrote a persistent Technician layer. If a node hangs or hits a port collision (hello `WinError 10048`), the Technician detects it, diagnoses the stack, kills zombie processes, reallocates networking, and brings the service back online with zero human intervention. * *1,600+ Industry Verticals:* Rather than building general-purpose workflows, the OS comes pre-loaded with state machines and hardcoded context for over 1,600 specific verticals running 1,284 autonomous workflows—handling everything from healthcare compliance to real estate out-of-the-box. * *Military-Grade Security & Built-in VPN:* The entire system runs inside an integrated, zero-trust VPN layer with cryptographic audit trails and multi-tenant isolation. Production-ready for DoD contracts, healthcare (HIPAA), and enterprise compliance. * *Deep Microsoft 365 Integration:* Full Microsoft Graph API implementation with native access to email, calendar, Teams, OneDrive, SharePoint, and Planner. The system orchestrates across AWS, Azure, and GCP with vertical-specific API frameworks for healthcare EHRs, real estate MLS systems, and finance platforms.

*Why did I build this?* Started with API keys, ended with self-evolving, self-healing Systems AGI... and I still have to retrieve my own API keys lol. At least the system is acquisition-ready, fully decoupled, heals its own memory leaks and stalled sockets natively, and scales horizontally.

Happy to answer any questions about the 13-layer self-healing mechanism, managing the websockets across a 12-server swarm without race conditions, or the multi-agent consensus algorithms. *Serious acquisition inquiries:* sophify-aaron@hotmail.com Tear it apart. What did I miss?

Comments URL: https://news.ycombinator.com/item?id=47230379

Points: 1

# Comments: 0

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Building vs Digging

Tue, 03/03/2026 - 5:00am
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Time to First Success

Tue, 03/03/2026 - 4:58am
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Ask HN: How do you verify crypto payments in your app?

Tue, 03/03/2026 - 4:56am

I'm building a SaaS that accepts USDC payments. Simple requirement: customer sends stablecoins, I need to know when they arrived and the amount matches. Current options seem to be:

1. Payment processor (Coinbase Commerce, NOWPayments) — handles everything but takes 0.5-1% per transaction. On a $10k payment that's $50-100 just for confirmation.

2. DIY with Alchemy/Infura webhooks — poll for Transfer events, match amounts, track confirmations, handle reorgs, build retry logic. Took me ~2 weeks and it's still brittle.

Is there a middle ground? Something that just does the verification part — "tell me when X amount arrived at Y address" — without the custody/checkout/fee overhead? Curious how others are handling this, especially on L2s like Base where tx costs are negligible but verification infra is the same complexity as mainnet.

Comments URL: https://news.ycombinator.com/item?id=47230346

Points: 1

# Comments: 0

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